Author Affiliations
1School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui 241002, China2Anhui Provincial Engineering Research Center for Information Fusion and Control of Intelligent Robots, Wuhu, Anhui 241002, China3Wuhan Mingke Rail Transit Equipment Co., Ltd., Wuhan, Hubei 430074, Chinashow less
Fig. 1. YOLOv5 network structure
Fig. 2. Fog image
Fig. 3. Structure diagram of C3 and PC3
Fig. 4. Conv and PConv
Fig. 5. Distribution of label aspect ratio
Fig. 6. YOLOv5 default path and EFPN path
Fig. 7. Removing the path EFPN' from the large object detection layer
Fig. 8. CBAM attention mechanism
Fig. 9. Improved network structure
Fig. 10. Distribution of traffic signs after expansion
Fig. 11. Comparison of accuracy effects of traffic signs before and after improvement
Fig. 12. Comparison of missed detection effects of traffic signs before and after improvement
Fig. 13. Comparison of false detection effects of traffic signs before and after improvement
改进方法 | 精确率P/% | Params/M | pn | w32 | ph4 | 模型说明:YOLOv5+PC3+EFPN表示YOLOv5中C3中的普通卷积用轻量化部分卷积PConv替换,构成PC3替换掉原C3结构,检测头用EFPN替换;YOLOv5+PC3+EFPN'表示YOLOv5中C3中的普通卷积用轻量化部分卷积PConv替换,构成PC3替换掉原C3结构,检测头用EFPN'替换。 | YOLOv5+PC3+EFPN | 0.93 | 0.70 | 0.76 | 4.96 | YOLOv5+PC3+ EFPN ' | 0.96 | 0.96 | 0.98 | 3.80 |
|
Table 1. Comparison of EFPN and EFPN' structures
检测尺度 | Anchor1 | Anchor2 | Anchor3 | 小尺寸 | [10,13] | [16,30] | [33,23] | 中尺寸 | [30,61] | [62,45] | [59,119] | 大尺寸 | [116,90] | [156,198] | [373,326] |
|
Table 2. YOLOv5 default anchor box size
检测尺度 | Anchor1 | Anchor2 | Anchor3 | 小尺寸 | [5,5] | [6,7] | [8,9] | 中尺寸 | [9,14] | [9,14] | [14,15] | 大尺寸 | [19,20] | [19,20] | [25,26] |
|
Table 3. Results of K-means clustering algorithm
编号 | 模型 | P | R | mAP0.5 | FPS | L/ms | Params/M | 模型说明:FOG代表扩充雾化数据集TT100K-FOG;PC3代表使用更加轻量的PConv构建PC3特征提取模块来取代YOLOv5骨干和颈部网络中的C3模块;EFPN代表采用延伸的特征金字塔结构,替代YOLOv5中检测头;EFPN'代表在EFPN结构中删除大目标检测层后,替代YOLOv5中检测头;Focal-EIoU代表采用Focal-EloU取代YOLOv5默认函数CIoU;CBAM代表在YOLOv5主干网络中嵌入空间和通道注意力模块。 | 0 | YOLOv5 | 0.842 | 0.824 | 0.861 | 145.7 | 6.9 | 7.10 | 1 | YOLOv5+FOG | 0.893 | 0.838 | 0.870 | 145.7 | 6.9 | 7.10 | 2 | YOLOv5+FOG+PC3 | 0.853 | 0.768 | 0.840 | 166.7 | 6.0 | 4.87 | 3 | YOLOv5+FOG+PC3+EFPN | 0.862 | 0.759 | 0.842 | 135.1 | 7.4 | 4.96 | 4 | YOLOv5+FOG+PC3+EFPN' | 0.876 | 0.782 | 0.854 | 161.3 | 6.2 | 3.80 | 5 | YOLOv5+FOG+PC3+EFPN'+Focal-EIoU | 0.906 | 0.790 | 0.860 | 161.3 | 6.2 | 3.80 | 6 | YOLOv5+FOG+PC3+EFPN'+Focal-EIoU+CBAM | 0.917 | 0.853 | 0.899 | 151.5 | 6.7 | 3.95 |
|
Table 4. Results of ablation experiment
模型 | 平台 | 主干网 | 类型 | P/% | mAP0.5 /% | FPS | Faster R-CNN | MMDetection | ResNet50 | Anchor-based | 71.9 | 79.9 | 57.7 | YOLOv4 | Darknet | Darknet | Anchor-based | 58.7 | 82.2 | 80.9 | YOLOv5 | YOLOv5 | Darknet | Anchor-based | 84.2 | 86.1 | 145.7 | YOLOX | MMDetection | Darknet | Anchor-free | 72.6 | 79.7 | 93.6 | YOLOv6 | YOLOv6 | EfficientRep | Anchor-free | 77.7 | 81.1 | 162.8 | YOLOv7 | YOLOv7 | E-ELAN | Anchor-based | 72.0 | 77.4 | 130.2 | YOLOv8 | YOLOv8 | Darknet | Anchor-free | 87.7 | 83.7 | 171.4 | Ours | YOLOv5 | Darknet | Anchor-based | 91.7 | 89.9 | 151.5 |
|
Table 5. Performance comparison with other algorithms